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#!/usr/bin/env python3
"""
Quick Model Deployment Script
Direct deployment without argument parsing issues
"""
import os
import sys
import logging
from pathlib import Path
# Add src to path for imports
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
# Setup logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
def main():
"""Direct deployment without argument parsing"""
# Configuration
MODEL_PATH = "/output-checkpoint"
REPO_NAME = "Tonic/smollm3-finetuned"
HF_TOKEN = os.getenv('HF_TOKEN')
if not HF_TOKEN:
logger.error("❌ HF_TOKEN not set")
return 1
if not Path(MODEL_PATH).exists():
logger.error(f"❌ Model path not found: {MODEL_PATH}")
return 1
logger.info("✅ Model files validated")
# Import and run the recovery pipeline directly
try:
from recover_model import ModelRecoveryPipeline
# Initialize pipeline
pipeline = ModelRecoveryPipeline(
model_path=MODEL_PATH,
repo_name=REPO_NAME,
hf_token=HF_TOKEN,
private=False,
quantize=True,
quant_types=["int8_weight_only", "int4_weight_only"],
author_name="Tonic",
model_description="A fine-tuned SmolLM3 model for improved text generation and conversation capabilities"
)
# Run the complete pipeline
success = pipeline.run_complete_pipeline()
if success:
logger.info("✅ Model deployment completed successfully!")
logger.info(f"🌐 View your model at: https://huggingface.co/{REPO_NAME}")
return 0
else:
logger.error("❌ Model deployment failed!")
return 1
except Exception as e:
logger.error(f"❌ Error during deployment: {e}")
return 1
if __name__ == "__main__":
exit(main()) |